Clustering exploration for SCPCS000172
Maud PLASCHKA
2024-08-13
Introduction
subdiagnosis <- readr::read_tsv(
file.path("..", "..", "..", "data", "current", params$scpca_project_id, "single_cell_metadata.tsv"),
show_col_types = FALSE
) |>
dplyr::filter(scpca_sample_id == params$sample_id) |>
dplyr::pull(subdiagnosis)The aim is to explore the clustering and label transfers for the sample SCPCS000172. This sample has a(n) Favorable subdiagnosis.
In order to explore the clustering results, we look into some marker genes, pathways enrichment and label transfer.
This approach would provide us a rapid idea of the quality of the clustering:
- in the case each cluster do have a set of specific marker genes, we could expect each of the cluster to have at least a different phenotype,
- if marker genes are mostly shared between clusters, we might have overclustered,
- if no marker genes are found, the quality of cells in the clusters might be impaired (high mitochondrial content? ribosomal content?)
Packages
Base directories
# The base path for the OpenScPCA repository, found by its (hidden) .git directory
repository_base <- rprojroot::find_root(rprojroot::is_git_root)
# The current data directory, found within the repository base directory
data_dir <- file.path(repository_base, "data", "current", params$scpca_project_id)
# The path to this module
module_base <- file.path(repository_base, "analyses", "cell-type-wilms-tumor-06")Input files
In this notebook, we are working with the Wilms tumor sample defined
in SCPCS000172 from the Wilms tumor dataset SCPCP000006. We work with
the pre-processed and labeled Seurat object that is the
output of
02b_label-transfer_fetal_kidney_reference_Stewart.Rmd saved
in the results directory.
To explore the clustering results, we look into some marker genes reported here:
Output file
Reports will be saved in the notebook directory. The
pre-processed and annotated Seurat object per samples are
saved in the result folder.
Functions
Here we defined function that will be used multiple time all along the notebook.
Visualize seurat clusters and metadata
For a Seurat object objectand a metadata
metadata, the function visualize_metadata will
plot FeaturePlot and BarPlot
objectis the Seurat objectmetadatathe gene or quantitative value to be plottedgroup.byis the metadata used for grouping the violin plots
visualize_metadata <- function(object, meta, group.by){
if(is.numeric(object@meta.data[,meta])){
d <- SCpubr::do_FeaturePlot(object,
features = meta,
pt.size = 0.2,
legend.width = 0.5,
legend.length = 5,
legend.position = "right") + ggtitle(meta)
b <- SCpubr::do_ViolinPlot(srat,
features = meta,
ncol = 1,
group.by = group.by,
legend.position = "none")
return(d + b + plot_layout(ncol = 2, widths = c(2,4)))
}
else{
d <- SCpubr::do_DimPlot(object, reduction="umap", group.by = group.by, label = TRUE, repel = TRUE) + ggtitle(paste0(meta," - umap")) + theme(text=element_text(size=18))
b <- SCpubr::do_BarPlot(sample = object,
group.by = meta,
split.by = group.by,
position = "fill",
font.size = 10,
legend.ncol = 3) +
ggtitle("% cells")+
xlab(print(group.by)) +
theme(text=element_text(size=18))
return(d + b + plot_layout(ncol = 2, widths = c(2,4)))
}
}Visualize seurat clusters and markers genes
For a Seurat object objectand a features
features, the function visualize_feature will
plot FeaturePlot and ViolinPlot
objectis the Seurat objectfeaturesthe gene or quantitative value to be plottedgroup.byis the metadata used for grouping the violin plots
visualize_feature <- function(object, features, group.by = "seurat_clusters"){
feature_symbol <- AnnotationDbi::select(org.Hs.eg.db,
keys=features,
columns="SYMBOL",
keytype="ENSEMBL")
d <- SCpubr::do_FeaturePlot(object,
features = feature_symbol$ENSEMBL,
pt.size = 0.2,
legend.width = 0.5,
legend.length = 5,
legend.position = "right") + ggtitle(feature_symbol$SYMBOL)
b <- SCpubr::do_ViolinPlot(srat,
features = feature_symbol$ENSEMBL,
ncol = 1,
group.by = group.by,
legend.position = "none",
assay = "SCT") + ylab(feature_symbol$SYMBOL)
return(d + b + plot_layout(ncol = 2, widths = c(2,4)))
}Calculate ModuleScore from a MSigDB
dataset
For a Seurat object object, the function
MSigDB_score will calculate a score using
AddModuleScore() function for a MSigDB gene set
gs_name in the category category
objectis the Seurat object to calculate the score. Score will be added in the metadata of this object.categoryis a MSigDB collection (https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp). values inc("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8")gs_nameis the name of a MSigDB gene set, e.g."HALLMARK_P53_PATHWAY"nameis the name of the module
MSigDB_score <- function(object, category, gs_name, name){
set <- msigdbr(species = "human", category = category)
set_list <- set %>%
dplyr::filter(gs_name == gs_name) %>%
dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()
set_list <- list(set_list$human_ensembl_gene)
suppressWarnings({
object <- AddModuleScore(object, features = set_list, name = name)
})
return(object)
}Enrichment analysis
Enrichment_plotaim to perform enrichment of the marker
genes for each of the Seurat clusters and summarize the results in a
dotplot.
categoryis the MSigDB category or collection to be used values inc("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8")signaturesis a list of marker genes per clusterbackgroundis the universe used for enrichment
Enrichment_plot <- function(category, signatures, background){
## define genesets
tryCatch( expr = {
gene_set <- msigdbr(species = "human", category = category)
msigdbr_set <- gene_set %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()
cclust<-compareCluster(geneCluster = signatures,
fun = enricher,
TERM2GENE = msigdbr_set,
universe=background)
d <- dotplot(cclust,showCategory=15) + scale_y_discrete(labels=function(x) str_wrap(x, width=40))
return(d)
},
error = function(e) { print("No enrichment") } )
}do_Table_Heatmap
do_Table_Heatmap shows heatmap of counts of cells for
combinations of two metadata variables
dataseurat objectfirst_groupis the name of the first metadata to group the cellslast_groupis the name of the second metadata to group the cells
do_Table_Heatmap <- function(data, first_group, last_group ){
df <- data@meta.data %>%
mutate_if(sapply(data@meta.data, is.character), as.factor) %>%
group_by( !!sym(first_group), !!sym(last_group))%>%
summarise(Nb = log(n()))
p <- ggplot(df, aes(x= !!sym(first_group), y = !!sym(last_group), fill = Nb)) +
geom_tile() +
scale_fill_viridis_c() +
theme_bw() +
theme(text = element_text(size = 20)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
guides(fill=guide_legend(title=" Number of \n cells (log)"))
return(p)
}Analysis
Load the pre-processed Seurat object
Visualize seurat clusters
We expect up to 5 set of clusters:
- blastema cancer cells
- epithelium cancer and/or normal cells
- stroma cancer and/or normal cells
- immune cells
- endothelial cells
d2 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - umap")
d1 <- SCpubr::do_DimPlot(srat, reduction="pca", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - pca")
v1 <- SCpubr::do_ViolinPlot(srat, features = c( "subsets_mito_percent"), ncol = 1, group.by = "seurat_clusters", legend.position = "none")
v2 <- SCpubr::do_ViolinPlot(srat, features = c( "detected"), ncol = 1, group.by = "seurat_clusters", legend.position = "none")
v3 <- SCpubr::do_ViolinPlot(srat, features = c( "sum"), ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + (v1 + v2 +v3 + plot_layout(ncol=1) ) + plot_layout(ncol = 3, widths = c(2,2,4))
Cell cycle information
s.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$s.genes]
g2m.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$g2m.genes]
srat <- CellCycleScoring(srat, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE)## [1] "seurat_clusters"
Look at specific genes
Here, we open the table of marker genes marker-sets/CellType_metadata.csv. Note: we do not expect to have a clear and nice pattern of expression for all of the following markers in every tumor. This is just ti get a few idea.
for(feature in CellType_metadata$ENSEMBL_ID[CellType_metadata$ENSEMBL_ID %in% rownames(srat@assays$SCT)]){
print(visualize_feature(srat, features = feature, group.by = "seurat_clusters"))
}
Look at specific pathways
TP53 pathway
here we will calculate a TP53 score using AddModuleScore
and the genes of the HALLMARK_P53_PATHWAY gene set.
srat <- MSigDB_score(object = srat , category = "H", gs_name = "HALLMARK_P53_PATHWAY", name = "TP53_score")
visualize_metadata(srat, meta = "TP53_score1", group.by = "seurat_clusters" )
DNA repair pathway
here we will calculate a DNA_repair score using
AddModuleScore and the genes of the HALLMARK_DNA_REPAIR
gene set.
srat <- MSigDB_score(object = srat , category = "H", gs_name = "HALLMARK_DNA_REPAIR", name = "DNA_repair_score")
visualize_metadata(srat, meta = "DNA_repair_score1", group.by = "seurat_clusters" )
Note: Chemo-treated samples should have higher DNA-damage scores.
Find marker genes for each of the seurat clusters
In addition to the list of known marker genes, we used an unbiased approach to find transcripts that characterized the different clusters.
We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method. Please check the preprint from Chistoph Hafemeister: https://www.biorxiv.org/content/10.1101/2023.03.28.534443v1 and tool described here: https://github.com/cancerbits/DElegate
Of note, we won’t use it for annotation, this is just here to get an idea!
feature_conversion <- srat@assays$RNA@meta.data
de_results <- DElegate::FindAllMarkers2(srat, group_column = "seurat_clusters")
#filter the most relevant markers
s.markers <- de_results[de_results$padj < params$padj_thershold & de_results$log_fc > params$lfc_threshold & de_results$rate1 > params$rate1_threshold,]
# add gene symbol for easiest interpretation of the result
s.markers$gene_ids <- s.markers$feature
s.markers <- left_join(s.markers,feature_conversion, by = c( "gene_ids") )
stopifnot(
"Error joining gene ids and feature names" = identical(s.markers$feature, s.markers$gene_ids)
)DT::datatable(s.markers, caption = ("marker genes"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))# Select top 5 genes for heatmap plotting
s.markers <- na.omit(s.markers)
s.markers %>%
group_by(group1) %>%
top_n(n = 5, wt = log_fc) -> top5
# subset for plotting
cells <- WhichCells(srat, downsample = 100)
ss <- subset(srat, cells = cells)
ss <- ScaleData(ss, features = top5$feature)
p1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
p2 <- DoHeatmap(ss, features = top5$feature, cells = cells, group.by = "seurat_clusters") + NoLegend() +
scale_fill_gradientn(colors = c("#01665e","#35978f",'darkslategray3', "#f7f7f7", "#fee391","#fec44f","#F9AD03"))
p3 <- ggplot(srat@meta.data, aes(seurat_clusters, fill = seurat_clusters)) + geom_bar() + NoLegend()
common_title <- sprintf("Unsupervised clustering %s, %d cells", srat@meta.data$orig.ident[1], ncol(srat))
show((((p1 / p3) + plot_layout(heights = c(3,2)) | p2) ) + plot_layout(widths = c(1, 2)) + plot_layout(heights = c(3,1)) + plot_annotation(title = common_title))
Enrichment analysis of marker genes
Here we perform enrichment analysis of the marker genes found in the previous section for each Seurat cluster.
We defined as universe/background all the genes expressed in the dataset, meaning the rownames of the Seurat object.
We used three gene sets from MSiGDB:
- the hallmark gene sets are coherently expressed signatures derived by aggregating many MSigDB gene sets to represent well-defined biological states or processes.
- the C3 : regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites.
- the C8 : cell type signature gene sets curated from cluster markers identified in single-cell sequencing studies of human tissue.
We used enricher function from clusterProfiler to perform enrichment analysis.
# define background genes = universe for enrichment
background <- rownames(srat)
# Define gene signature per cluster
signatures <- list()
for( i in unique(s.markers$group1)){
signatures[[paste0("cluster ",i)]] <- s.markers$feature[s.markers$group1 == i]
}Hallmarks MSiGDB gene sets
EMT signature should be enriched in stroma cluster. E2F/proliferation should be enriched in blastema cluster. MYC(N), TP53 must be enriched in blastema cluster.
C8 MSiGDB cell type signature gene sets
The MSigDB C8 gene set is quite relevant for kidney and nephroblastoma annotations. Epithelial (cancer and normal) cells should be enriched in mature/adulte kidney pathways while blastema cancer cells will show enrichment of fetal kidney development pathway / cap mesenchyme.
Cell annotation
Annotation from SingleR (no cancer or kidney specific dataset)
Here, we quickly checked annotations that are present in the _processed rds object. However, the automated annotation have not been performed using a cancer specific reference or a kidney reference. We do not expect a nice labelling of the cells as the overlap of cell types between the reference and the query dataset is poor. This support the need to do a proper label transfer from the fetal kidney atlas, which is imho the best reference that can be applied to a Wilms tumor query.
## [1] "seurat_clusters"
Annotation from cellassign (no cancer or kidney specific dataset)
## [1] "seurat_clusters"
Annotation from the fetal reference (Cao et al)
## [1] "seurat_clusters"
## [1] "seurat_clusters"
## [1] "seurat_clusters"
Annotation from the kidney reference (Stewart et al)
## [1] "seurat_clusters"
## [1] "seurat_clusters"
Compare annotations with marker genes
Here we evaluate with marker genes the identification of endothelial, immune cells and other cancer and non-cancerous sub-populations.
markers = list("malignant" = CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "malignant")],
"immune"= CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "immune")],
"endothelium"= CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "endothelium")],
"non-malignant"= CellType_metadata$ENSEMBL_ID[which(CellType_metadata$cell_class == "non-malignant")])
p1 <- SCpubr::do_DotPlot(sample = srat,
group.by = "fetal_kidney_predicted.compartment",
features = markers,
axis.text.x.angle = 90,
flip = TRUE,
plot.title = "fetal_kidney_predicted.compartment"
)
p2 <- SCpubr::do_DotPlot(sample = srat,
group.by = "fetal_kidney_predicted.cell_type",
features = markers,
axis.text.x.angle = 90,
flip = TRUE,
plot.title = "fetal_kidney_predicted.cell_type"
)
p3 <- SCpubr::do_DotPlot(sample = srat,
group.by = "fetal_full_predicted.annotation.l1",
features = markers,
axis.text.x.angle = 90,
flip = TRUE,
plot.title = "fetal_full_predicted.annotation.l1"
)
p4 <- SCpubr::do_DotPlot(sample = srat,
group.by = "fetal_full_predicted.annotation.l2",
features = markers,
axis.text.x.angle = 90,
flip = TRUE,
plot.title = "fetal_full_predicted.annotation.l2"
)
p5 <- SCpubr::do_DotPlot(sample = srat,
group.by = "seurat_clusters",
features = markers,
axis.text.x.angle = 90,
flip = TRUE,
plot.title = "seurat_clusters"
)
p6 <- SCpubr::do_DotPlot(sample = srat,
group.by = "cellassign_celltype_annotation",
features = markers,
axis.text.x.angle = 90,
flip = TRUE,
plot.title = "cellassign_celltype_annotation"
)
(p1|p2)/(p3|p4) /(p5|p6)
Compare annotations between them
Annotation versus Seurat clusters
Here we check wether one (or more) Seurat cluster do
correspond to one label. This would allow us to be more confident in the
annotation of immune or endothelial cells for example.
p1 <- do_Table_Heatmap( srat,
last_group = "fetal_kidney_predicted.compartment",
first_group = "seurat_clusters"
)
p2 <- do_Table_Heatmap( srat,
last_group = "fetal_full_predicted.annotation.l1",
first_group = "seurat_clusters"
)
p3 <- do_Table_Heatmap( srat,
last_group = "singler_celltype_annotation",
first_group = "seurat_clusters"
)
p4 <- do_Table_Heatmap( srat,
last_group = "cellassign_celltype_annotation",
first_group = "seurat_clusters"
)
(p1|p2)/(p3|p4)
Comparison of the labels from the two fetal references
The use of the right reference is crucial for label transfer and annotation. It is recommended that the cell types in the reference is representative to the cell types to be annotated in the query.
Wilms tumors can contain up to three histologies that resemble fetal kidney: blastema, stroma, and epithelia [1, 2]. Because of their histological similarity to fetal kidneys, Wilms tumors are thought to arise from developmental derangements in embryonic renal progenitors.
In the previous step of the analysis, we selected two fetal references containing kidney samples and transfer the labels.
Here we aim to understand the overlap and differences between the two references.
Human fetal kidney atlas Stewart et al.
Stewart et al. created a human fetal kidney atlas [3].
This reference contains only fetal kidney cells and has been precisely annotated by kidney experts.
Azimuth Human fetal atlas Cao et al.
Azimuth also provides a human fetal atlas as a reference
[4].
The data can be found on Zenodo.
The reference contain cells from 15 organs including kidney from fetal samples.
We were first nicely surprised that most of the cells in the Wilms
tumor dataset mapped to “kidney” organ (see previous report
02a_fetal_all_reference_Cao_{sample_id}.html).
Session Info
# record the versions of the packages used in this analysis and other environment information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
## [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Vienna
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggalluvial_0.12.5 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0 clusterProfiler_4.12.6 enrichplot_1.24.4
## [6] msigdbr_7.5.1 sctransform_0.4.1 SeuratData_0.2.2.9001 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
## [11] Biobase_2.64.0 GenomicRanges_1.56.1 GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1
## [16] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.3.0 patchwork_1.2.0 lubridate_1.9.3
## [21] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
## [26] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 SCpubr_2.0.2
## [31] Azimuth_0.5.0 shinyBS_0.61.1 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4
## [36] optparse_1.7.5
##
## loaded via a namespace (and not attached):
## [1] R.methodsS3_1.8.2 vroom_1.6.5 poweRlaw_0.80.0 goftest_1.2-3
## [5] DT_0.33 Biostrings_2.72.1 vctrs_0.6.5 spatstat.random_3.3-1
## [9] digest_0.6.37 png_0.1-8 ggrepel_0.9.5 deldir_2.0-4
## [13] parallelly_1.38.0 MASS_7.3-61 Signac_1.14.0 reshape2_1.4.4
## [17] httpuv_1.6.15 qvalue_2.36.0 withr_3.0.1 xfun_0.47
## [21] ggfun_0.1.6 survival_3.7-0 EnsDb.Hsapiens.v86_2.99.0 memoise_2.0.1
## [25] gson_0.1.0 DElegate_1.2.1 tidytree_0.4.6 zoo_1.8-12
## [29] gtools_3.9.5 pbapply_1.7-2 R.oo_1.26.0 KEGGREST_1.44.1
## [33] promises_1.3.0 httr_1.4.7 restfulr_0.0.15 globals_0.16.3
## [37] fitdistrplus_1.2-1 rhdf5filters_1.16.0 rhdf5_2.48.0 rstudioapi_0.16.0
## [41] UCSC.utils_1.0.0 miniUI_0.1.1.1 generics_0.1.3 DOSE_3.30.5
## [45] babelgene_22.9 curl_5.2.2 zlibbioc_1.50.0 ggraph_2.2.1
## [49] polyclip_1.10-7 GenomeInfoDbData_1.2.12 SparseArray_1.4.8 xtable_1.8-4
## [53] pracma_2.4.4 evaluate_0.24.0 S4Arrays_1.4.1 hms_1.1.3
## [57] irlba_2.3.5.1 colorspace_2.1-1 hdf5r_1.3.11 getopt_1.20.4
## [61] ROCR_1.0-11 reticulate_1.38.0 spatstat.data_3.1-2 magrittr_2.0.3
## [65] lmtest_0.9-40 later_1.3.2 viridis_0.6.5 ggtree_3.12.0
## [69] lattice_0.22-6 spatstat.geom_3.3-2 future.apply_1.11.2 shadowtext_0.1.4
## [73] scattermore_1.2 XML_3.99-0.17 cowplot_1.1.3 RcppAnnoy_0.0.22
## [77] pillar_1.9.0 nlme_3.1-166 pwalign_1.0.0 caTools_1.18.2
## [81] compiler_4.4.1 RSpectra_0.16-2 stringi_1.8.4 tensor_1.5
## [85] GenomicAlignments_1.40.0 plyr_1.8.9 crayon_1.5.3 abind_1.4-5
## [89] BiocIO_1.14.0 gridGraphics_0.5-1 googledrive_2.1.1 locfit_1.5-9.10
## [93] graphlayouts_1.1.1 bit_4.0.5 fastmatch_1.1-4 codetools_0.2-20
## [97] crosstalk_1.2.1 bslib_0.8.0 plotly_4.10.4 mime_0.12
## [101] splines_4.4.1 Rcpp_1.0.13 fastDummies_1.7.4 sparseMatrixStats_1.16.0
## [105] cellranger_1.1.0 knitr_1.48 blob_1.2.4 utf8_1.2.4
## [109] seqLogo_1.70.0 AnnotationFilter_1.28.0 fs_1.6.4 listenv_0.9.1
## [113] ggplotify_0.1.2 Matrix_1.7-0 statmod_1.5.0 tzdb_0.4.0
## [117] tweenr_2.0.3 pkgconfig_2.0.3 tools_4.4.1 cachem_1.1.0
## [121] RSQLite_2.3.7 viridisLite_0.4.2 DBI_1.2.3 fastmap_1.2.0
## [125] rmarkdown_2.28 scales_1.3.0 grid_4.4.1 ica_1.0-3
## [129] shinydashboard_0.7.2 Rsamtools_2.20.0 sass_0.4.9 dotCall64_1.1-1
## [133] RANN_2.6.2 farver_2.1.2 scatterpie_0.2.4 tidygraph_1.3.1
## [137] yaml_2.3.10 rtracklayer_1.64.0 cli_3.6.3 leiden_0.4.3.1
## [141] lifecycle_1.0.4 uwot_0.2.2 presto_1.0.0 BSgenome.Hsapiens.UCSC.hg38_1.4.5
## [145] BiocParallel_1.38.0 annotate_1.82.0 timechange_0.3.0 gtable_0.3.5
## [149] rjson_0.2.22 ggridges_0.5.6 progressr_0.14.0 limma_3.60.4
## [153] parallel_4.4.1 ape_5.8 edgeR_4.2.1 jsonlite_1.8.8
## [157] RcppHNSW_0.6.0 TFBSTools_1.42.0 bitops_1.0-8 assertthat_0.2.1
## [161] bit64_4.0.5 Rtsne_0.17 yulab.utils_0.1.7 spatstat.utils_3.1-0
## [165] CNEr_1.40.0 highr_0.11 jquerylib_0.1.4 GOSemSim_2.30.2
## [169] shinyjs_2.1.0 SeuratDisk_0.0.0.9021 spatstat.univar_3.0-0 R.utils_2.12.3
## [173] lazyeval_0.2.2 shiny_1.9.1 htmltools_0.5.8.1 GO.db_3.19.1
## [177] rappdirs_0.3.3 ensembldb_2.28.1 glue_1.7.0 TFMPvalue_0.0.9
## [181] spam_2.10-0 googlesheets4_1.1.1 httr2_1.0.3 XVector_0.44.0
## [185] RCurl_1.98-1.16 rprojroot_2.0.4 treeio_1.28.0 BSgenome_1.72.0
## [189] gridExtra_2.3 JASPAR2020_0.99.10 igraph_2.0.3 R6_2.5.1
## [193] labeling_0.4.3 RcppRoll_0.3.1 GenomicFeatures_1.56.0 cluster_2.1.6
## [197] Rhdf5lib_1.26.0 gargle_1.5.2 aplot_0.2.3 DirichletMultinomial_1.46.0
## [201] DelayedArray_0.30.1 tidyselect_1.2.1 ProtGenerics_1.36.0 ggforce_0.4.2
## [205] future_1.34.0 munsell_0.5.1 KernSmooth_2.23-24 data.table_1.16.0
## [209] htmlwidgets_1.6.4 fgsea_1.30.0 RColorBrewer_1.1-3 rlang_1.1.4
## [213] spatstat.sparse_3.1-0 spatstat.explore_3.3-2 fansi_1.0.6